Automatic prediction of individual performance from "thin slices" of social behavior

Lepri, Bruno; Mana, Nadia; Cappelletti, Alessandro; Pianesi, Fabio

doi:10.1145/1631272.1631400

Cited by 26 publications

(18 citation statements)

References 16 publications

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“…The inference of performance in groups using nonverbal behavior has also received attention in recent years [19,11]. The most comprehensive work been by Woolley et al [19], in which the effect of collective intelligence (a novel way to characterize group composition) on group performance was studied on a range of cognitive tasks.…”

Section: Related Workmentioning

confidence: 99%

“…Nonverbal cues, particularly turn-taking and gaze patterns, are known to reveal social-psychological constructs such as traits [14], interpersonal perceptions [7] and performance [11,19]. Below, we describe and define the speaking and looking cues that capture a group's turn-taking and gaze behavior in multiple ways.…”

Section: Group Nonverbal Cue Extractionmentioning

confidence: 99%

“…Copyright 2012 ACM 978-1-4503-1467-1/12/10 ...$15.00. 7,11,19], characterizing group behavior in unsupervised frameworks to mine typical behaviors of groups is a relatively unexplored problem [8]. Such an approach can also yield intermediate representations that could help understand some group constructs in a holistic manner, because they describe groups based on co-occurence of observations, rather than directly choosing a measure like performance or satisfaction as it is done in supervised frameworks.…”

Section: Introductionmentioning

confidence: 99%

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Linking speaking and looking behavior patterns with group composition, perception, and performance

Kumar

Sanchez-Cortes

Otsuka³

et al. 2012

Proceedings of the 14th ACM International Conference on Multimodal Interaction

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This paper addresses the task of mining typical behavioral patterns from small group face-to-face interactions and linking them to social-psychological group variables. Towards this goal, we define group speaking and looking cues by aggregating automatically extracted cues at the individual and dyadic levels. Then, we define a bag of nonverbal patterns (Bag-of-NVPs) to discretize the group cues. The topics learnt using the Latent Dirichlet Allocation (LDA) topic model are then interpreted by studying the correlations with group variables such as group composition, group interpersonal perception, and group performance. Our results show that both group behavior cues and topics have significant correlations with (and predictive information for) all the above variables. For our study, we use interactions with unacquainted members i.e. newly formed groups.

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Section: Related Workmentioning

confidence: 99%

Section: Group Nonverbal Cue Extractionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Linking speaking and looking behavior patterns with group composition, perception, and performance

Kumar

Sanchez-Cortes

Otsuka³

et al. 2012

Proceedings of the 14th ACM International Conference on Multimodal Interaction

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“…Using an influence model, Dong et al [9] estimated functional roles in meetings related to tasks and socio-emotional roles on the MS Corpus. The work by Lepri et al [28] estimated individual performance from interaction slices. The above three works employed speaking activity cues, prosodic cues, and visual fidgeting cues.…”

Section: Human Behavior Analysis Using Infrastructurebased Sensorsmentioning

confidence: 99%

Privacy-sensitive recognition of group conversational context with sociometers

et al. 2011

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Recognizing the conversational context in which group interactions unfold has applications in machines that support collaborative work and perform automatic social inference using contextual knowledge. This paper addresses the task of discriminating one conversational context from another, specifically brainstorming from decision-making interactions, using easily computable nonverbal behavioral cues. Privacy-sensitive mobile sociometers are used to record the interaction data. We hypothesize that the difference in the conversational dynamics between brainstorming and decision-making discussions is significant and measurable using speaking activity-based nonverbal cues. We characterize the communication patterns of the entire group by the aggregation (both temporal and person-wise) of their nonverbal behavior. The results on our interaction data set show that the floor-occupation patterns in a brainstorming interaction are different from a decision-making interaction, and our method can obtain a classification accuracy as high as 87.5%.

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“…There is encouraging evidence that computers can be made capable of exploiting thin slices of behavior to detect individual traits such as personality [35,47] and dominance [27,28,31], group properties like social roles [17], interactions' outcomes [15,36], etc. Many of these works have employed so-called 'honest signals' -social signals that, being too difficult for humans to control, can provide a reliable source of information about socially relevant aspects [58,45].…”

Section: Sensing Social Signalsmentioning

confidence: 99%

Human Behavior Understanding for Inducing Behavioral Change: Application Perspectives

Salah

Lepri

Pianesi

et al. 2011

Lecture Notes in Computer Science

Self Cite

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Abstract. The 2nd International Workshop on Human Behavior Understanding (HBU'11) focuses on inducing behavioral change via computer systems that can analyse human behavior and communicate persuasive messages accordingly. While analysis techniques that involve pattern recognition, signal processing and machine learning are very relevant to this aim, the underlying psychological and sociological aspects of inducing behavioral change cannot be neglected. This paper provides a framework for assessing the impact of social factors for these applications, and discusses the role of social mediation of behaviors and attitudes.

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Automatic prediction of individual performance from "thin slices" of social behavior

Cited by 26 publications

References 16 publications

Linking speaking and looking behavior patterns with group composition, perception, and performance

Linking speaking and looking behavior patterns with group composition, perception, and performance

Privacy-sensitive recognition of group conversational context with sociometers

Human Behavior Understanding for Inducing Behavioral Change: Application Perspectives

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